Method for automatically following hand movements in an image sequence

ABSTRACT

A method for following hand movements in an image flow, includes receiving an image flow in real time, locating in each image in the received image flow a hand contour delimiting an image zone of the hand, extracting the postural characteristics from the image zone of the hand located in each image, and determining the hand movements in the image flow from the postural characteristics extracted from each image. The extraction of the postural characteristics of the hand in each image includes locating in the image zone of the hand the center of the palm of the hand by searching for a pixel of the image zone of the hand the furthest from the hand contour.

TECHNICAL FIELD

The present disclosure generally relates to image processing and theinterfaces enabling a device such as a computer to be controlled.

The present disclosure relates more particularly but not exclusively tofollowing hand movement in real time in an image flow supplied forexample by a video camera.

BACKGROUND INFORMATION

To man, hand gestures are a natural and intuitive method ofcommunication to interact with the environment. These gestures can serveto emphasize speech, designate or handle objects, or even constitute alanguage in its own right, like sign language. The information conveyedby hand gestures proves to be much richer than the information that canbe supplied using a keyboard or a pointing device such as a mouse. It isthus desirable to use hand gestures to control a device such as acomputer.

Therefore, using electronic gloves equipped with sensors supplying theposition of the hand and the angles of the finger joints has alreadybeen suggested. However, such gloves prove to be relatively cumbersomeand costly due to the number of sensors necessary to determine theposition of the fingers.

Furthermore, the regular increase in the power of desktop computers andthe emergence of cheap video cameras, enable the production of areal-time hand gesture recognition system, suited to desktop computers,to be considered.

Now, the recognition of hand gestures in a sequence of images initiallyrequires locating the contours of the hand in each image in thesequence. For this purpose, certain methods in prior art require theuser to wear a colored glove. However, these methods are very sensitiveto luminosity variations, to shadows and to changes in background image.In addition, wearing such a glove proves to be unpleasant for the user.

Secondly, hand gesture recognition requires the postural characteristicsof the hand located in each image to be determined, and finally handmovements from one image to the next to be followed from the posturalcharacteristics.

The postural characteristics of the hand can be analyzed for example bypositioning a skeletal pattern on the hand. However, this techniquerequires precisely determining the center of the hand. Classically, thecenter of the hand is determined by calculating the center of gravity ofthe region delimited by the contours of the hand, from the geometrictorque. If the hand also contains the forearm, the center of gravitycalculated will be shifted towards the center of the arm.

Furthermore, hand movements are generally followed using a pattern thatis difficult to initialize by making hypotheses about the configurationof the hand.

BRIEF SUMMARY

One or more embodiments overcome all or part of the disadvantagesexplained above.

One embodiment of a method for following hand movements in an imageflow, comprises:

-   -   receiving an image flow in real time,    -   locating in each image in the image flow received a hand contour        delimiting an image zone of the hand,    -   extracting postural characteristics from the image zone of the        hand located in each image, and    -   determining the hand movements in the image flow from the        postural characteristics extracted from each image.

According to one embodiment, the extraction of the posturalcharacteristics of the hand in each image comprises locating in theimage zone of the hand the center of the palm of the hand includingsearching for a pixel of the image zone of the hand the furthest fromthe hand contour.

According to one embodiment, the location of the center of the palm ofthe hand comprises applying to the image zone of the hand a distancetransform which associates with each pixel of the image zone of the handthe distance from the pixel to the nearest pixel of the hand contour,the center of the palm of the hand being located on the pixel associatedwith the greatest distance.

According to one embodiment, the postural characteristics of the handcomprise the positions in the image of the fingertips of the hand and ofthe hollows between each finger, these positions being determined byassociating with each pixel of the hand contour the distance between thecontour pixel and the pixel of the center of the palm of the hand, thefingertips being located on the hand contour pixels associated withlocal maxima of variations in the distance with the center of the palmof the hand along the hand contour, and the hollows between the fingersbeing located on the hand contour pixels associated with local minima ofvariations in the distance with the center of the palm of the hand alongthe hand contour.

According to one embodiment, the postural characteristics of the handcomprise the positions in the image of the bases of the fingers of thehand, the base of each finger being positioned in the middle of thesegment delimited by the two hollows of the finger.

According to one embodiment, the postural characteristics of the handcomprise the positions in the image of the bases of the fingers of thehand, the base of each finger being positioned:

-   -   by selecting the hollow closest to the fingertip,    -   by searching for a point on the hand contour located the closest        to the selected hollow, and at the same distance from the        fingertip as the selected hollow, and    -   by calculating the position of the base of the finger in the        middle of the segment delimited by the selected hollow and the        point found.

According to one embodiment, the method comprises searching for thethumb among fingers identified by a base and a fingertip, includingcalculating an angle between a forearm vector linking the position ofthe center of the palm of the hand to a position of the forearm in theimage of the hand, and a vector of each finger linking the base to thefingertip, the thumb being the finger forming the widest angle with thevector of the forearm.

According to one embodiment, the method comprises searching for thethumb among fingers identified by a base and a fingertip, includingdetermining the length or the width of the fingers, the thumb being theshortest or the widest finger.

According to one embodiment, the postural characteristics of the handcomprise the position of the beginning of the forearm which isdetermined in each image in the image flow by searching for the midpointof contiguous pixels of the hand contour the furthest from the center ofthe palm of the hand and from the fingertips.

According to one embodiment, the image zone of the hand in each image inthe image flow is located on the basis of the skin color of the hand.

According to one embodiment, a pixel of an image in the image flowbelongs to the image zone of the hand if its color components Cb, Cr inthe color space YCbCr meet the following relations:

77≦Cb≦127 and 133≦Cr≦173.

According to one embodiment, the hand contour in each image in the imageflow is refined by calculating a probability that each pixel of theimage belongs to the hand or not, and by applying a threshold to theprobability to obtain a binary image consisting of pixels belonging tothe hand or not.

According to one embodiment, the probability that a pixel i of an imagein the image flow belongs to the hand is calculated using the followingformula:

${p(i)} = \frac{{hskin}(i)}{{htot}(i)}$

wherein hskin and htot represent the values of 2D chrominance histogramsfor the chrominance components Cb, Cr of the pixel i, the histogramhskin being established on the image zone of the hand, and htot beingestablished on the entire image.

According to one embodiment, median filtering and connected componentlabeling are successively applied to the binary image.

According to one embodiment, the location in each image in the receivedimage flow of a hand contour is limited to a reduced search zone in theimage, determined according to the position of the hand contour in aprevious image in the image flow.

According to one embodiment, the hand movements in the image flowcomprise a global movement determined using the displacement of theposition of the center of the palm of the hand, and a displacement ofeach finger of the hand determined by matching each fingertip positionbetween two successive images by minimizing the distance between eachfingertip position in the successive images, it only being possible tomatch each marker once between two successive images.

One embodiment also relates to a device for following hand movements inan image flow, comprising:

-   -   means for acquiring an image flow in real time,    -   a location module for locating in each image in the image flow a        hand contour delimiting an image zone of the hand,    -   an extraction module for extracting postural characteristics        from the image zone of the hand located in each image, and    -   a following module for determining the hand movements in the        image flow from the postural characteristics extracted from each        image.

According to one embodiment, the extraction module is configured todetermine the position of the center of the palm of the hand in theimage zone of the hand, by searching for a pixel of the image zone ofthe hand the furthest from the hand contour.

According to one embodiment, the extraction module is configured toapply to the image zone of the hand a distance transform whichassociates with each pixel of the image zone of the hand the distancefrom the pixel to the closest pixel of the hand contour, the center ofthe palm of the hand being located on the pixel associated with thegreatest distance.

According to one embodiment, the postural characteristics of the handcomprise the positions in the image of the fingertips of the hand and ofthe hollows between each finger, the extraction module being configuredto determine these positions by associating with each pixel of the handcontour the distance between the contour pixel and the pixel of thecenter of the palm of the hand, the fingertips being located on the handcontour pixels associated with local maxima of variations in thedistance with the center of the palm of the hand along the hand contour,and the hollows between the fingers being located on the hand contourpixels associated with local minima of variations in the distance withthe center of the palm of the hand along the hand contour.

According to one embodiment, the postural characteristics of the handcomprise the positions in the image of the bases of the fingers of thehand, the extraction module being configured to determine the positionof the base of each finger in the image zone of the hand by searchingfor the middle of the segment delimited by the two hollows of thefinger.

According to one embodiment, the postural characteristics of the handcomprise the positions in the image of the bases of the fingers of thehand, the extraction module being configured to determine the positionof the base of each finger:

-   -   by selecting the hollow closest to the fingertip,    -   by searching for a point on the hand contour located the closest        to the selected hollow, and at the same distance from the        fingertip as the selected hollow, and    -   by calculating the position of the base of the finger in the        middle of the segment delimited by the selected hollow and the        point found.

According to one embodiment, the extraction module is configured tosearch for the thumb among fingers identified by a base and a fingertip,by calculating an angle between a forearm vector linking the position ofthe center of the palm of the hand to a position of the forearm in theimage of the hand, and a vector of each finger linking the base to thefingertip, the thumb being the finger forming the widest angle with thevector of the forearm.

According to one embodiment, the extraction module is configured tosearch for the thumb among fingers identified by a base and a fingertip,comprising determining the length or the thickness of the fingers, thethumb being the longest or the widest finger.

According to one embodiment, the postural characteristics of the handcomprise the position of the beginning of the forearm, the extractionmodule being configured to determine this position by searching for themidpoint of contiguous pixels of the hand contour the furthest from thecenter of the palm of the hand and from the fingertips.

According to one embodiment, the location module is configured to locatethe image zone of the hand in each image in the image flow on the basisof the skin color of the hand.

According to one embodiment, the location module is configured to refinethe hand contour in each image in the image flow by calculating theprobability that each pixel of the image belongs to the hand or not, andby applying a threshold to the probability to obtain a binary imagecomprising pixels belonging to the hand or not.

According to one embodiment, the location module is configured tosuccessively apply median filtering and connected component labeling tothe binary image.

According to one embodiment, the location module is configured to locatethe hand contour in a reduced search zone determined according to theposition of the hand contour in a previous image in the image flow.

According to one embodiment, the hand movement following module isconfigured to determine a global movement of the hand using thedisplacement of the position of the center of the palm of the hand, anddetermining a displacement of each finger of the hand by matching eachfingertip position between two successive images by minimizing thedistance between each fingertip position in the successive images, itonly being possible to match each marker once between two successiveimages.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

These features shall be presented in greater detail in the followingdescription of one or more embodiments, given in relation with, but notlimited to the following figures, in which:

FIG. 1 represents in block form a system for following hand movementsaccording to one embodiment,

FIGS. 2 a, 2 b and 2 c are images of the hand, showing a method forextracting the contours of the hand in an image according to oneembodiment,

FIGS. 3 a, 3 b, 3 c and 3 d are examples of images of distance mapsobtained from hand contour images, in accordance with the methodaccording to one embodiment,

FIGS. 4 a, 4 b are images of hand contours, showing a method forapplying a skeletal pattern to the hand contour according to oneembodiment,

FIG. 5 is an example distance curve of the points of the hand contourwith the center of the hand,

FIG. 6 is a partial view of a contour image showing the determination ofthe joint of the base of a finger according to one embodiment.

DETAILED DESCRIPTION

In the following description, numerous specific details are given toprovide a thorough understanding of embodiments. The embodiments can bepracticed without one or more of the specific details, or with othermethods, components, materials, etc. In other instances, well-knownstructures, materials, or operations are not shown or described indetail to avoid obscuring aspects of the embodiments.

Reference throughout this specification to “one embodiment” or “anembodiment” means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment. Thus, the appearances of the phrases “in oneembodiment” or “in an embodiment” in various places throughout thisspecification are not necessarily all referring to the same embodiment.Furthermore, the particular features, structures, or characteristics maybe combined in any suitable manner in one or more embodiments.

The headings provided herein are for convenience only and do notinterpret the scope or meaning of the embodiments.

FIG. 1 represents a hand following system HFD according to oneembodiment. The system HFD comprises an extraction module for extractingthe hand contour HLOC receiving in real time a flow of digital images SVfrom an acquisition system ACQ such as a video camera, an extractionmodule for extracting the postural characteristics of the hand FEXT, anda hand movement following module FOLM. The module FEXT extracts thepostural characteristics of the hand in each contour image supplied bythe module HLOC. The module FOLM determines the hand movements betweeneach image from the postural characteristics extracted by the moduleFEXT and supplies hand movement information Ml about hand movementbetween two successive images.

FIGS. 2 a to 2 c show the processing performed by the module HLOC.Generally, to extract the contours of an object, there are two maincategories of methods. In the methods of the first category, extractingthe contours of an object comprises performing an image differencebetween a current image in which the object must be detected and areference image, or background image or a previous image. These methodsassume that the video camera is fixed, and are very sensitive toluminosity variations, to shadows and to background image changes.

In the methods of the second category, an object is detected using thecolor of the object compared to the colors of the image background. Thehuman skin proves to be well located in certain color spaces. Thedetection of contours based on the color is therefore commonly used todetect the hands or the face. Therefore, the color components Cb and Crof the skin are located in the following ranges of values of the colorspace YCbCr:

77≦Cb≦127 and 133≦Cr≦173  (1)

However, the skin color also varies according to the surroundingluminosity. In addition, whatever the category of the method used, ifthe object to be detected has a color close to that of other objectspresent in the image, the contours obtained through these methods willintegrate the contours of all the objects detected. The methods whichonly take chrominance into account are thus little efficient.

According to one embodiment, the detection of the contours of the handin an image is improved by calculating the probability p(i) that eachpixel i of each image in the sequence is a pixel of skin or of the imagebackground. This probability can be calculated in the following manner:

$\begin{matrix}{{p(i)} = \frac{{hskin}(i)}{{htot}(i)}} & (2)\end{matrix}$

wherein hskin(i) and htot(i) represent the values of 2D chrominancehistograms for the chrominance components Cb, Cr of the pixel i, thehistogram hskin being established on the zone of the image having acolor located in the ranges of color components defined by the relations(1), and htot being established on the entire image. The histograms areadvantageously updated periodically in one embodiment so as to take theluminosity variations into account.

The contours of the hand are then determined by applying a threshold tothe probability p(i) of each pixel i of the image thus calculated, whichenables a binary mask to be obtained, e.g., an image comprising black(=0) or white (=1) pixels. The black pixels (pixels the probability p(i)of which is lower than the threshold) correspond to the background ofthe image, and the white pixels (pixels the probability p(i) of which isgreater than the threshold) correspond to the image zone of the hand HA.The binary mask in FIG. 2 b is thus obtained from the image 2 a.

Finally, a median filter can be applied to the binary mask to removenoise. In addition, the contours of the hand can be refined by aconnected component labeling process enabling the small zones of blackpixels in zones of white pixels and the small zones of white pixels inzones of black pixels to be removed. Thus, the image in FIG. 2 c isobtained from the binary mask in FIG. 2 b.

The duration of the processing for locating the hand contour can bereduced by limiting the calculations described previously to a window ofthe image in which the hand is assumed to be, given the position of thehand in the previous image and/or of the hand movement during theprevious images. For this purpose, the module HLOC predicts a window inwhich the hand is assumed to be. To do so, it implements the standardKalman model in one embodiment. The standard Kalman model predicts thestatus of a system from the previous measurements performed on thelatter so as to minimize the prediction error covariance matrix.

The extraction module for extracting the characteristics of the handFEXT determines the center of the hand in each image (like the one inFIG. 2 c) supplied by the module HLOC. Then, the module FEXT determinesthe configuration of the hand in each image by positioning a simplifiedskeletal pattern of the hand therein.

According to one embodiment, the center of the hand is determined bysearching for the pixel of the image zone of the hand HA the furthestfrom the hand contour. This operation can be performed by applying adistance transform to the pixels of each image supplied by the moduleHLOC. The distance transform associates with each pixel of the image(FIG. 2 c) the distance from the pixel to the closest black pixel, ablack pixel being associated with a zero distance value. This transformenables a grey-scale image of distances like those in FIGS. 3 a to 3 dto be obtained. The further the pixels of the image in FIG. 2 c are froma black pixel, the lighter they are in the images in FIGS. 3 a to 3 d.Therefore, due to the shape of the hand, the center of the palm is atthe location of the pixel of the image of distances having the maximumvalue.

The skeletal pattern of the hand used is represented in FIGS. 4 a and 4b. This model comprises markers (marked by circles on FIGS. 4 a, 4 b)centered on the locations of the center of the palm of the hand PC,joints of the bases of the fingers B1-B5 detected (B1, B2, B3 in FIG. 4b), of the detected fingertips T1-T5 (T1, T2, T3 in FIG. 4 b), and ofthe beginning of the forearm AB. In this skeletal pattern, the marker ofthe center of the palm of the hand PC is linked by vectors to the markerof the beginning of the forearm AB and to the markers of the bases ofthe fingers B1-B5 detected. Each finger j (j varying from 1 to 5 in thecase in FIG. 4 a, and from 1 to 3 in the case in FIG. 4 b) comprises avector linking the marker of its base Bj to the marker of its tip Tj.The positions of the hollows between each finger are also marked onFIGS. 4 a, 4 b by crosses C1-C6 (C1-C4 in FIG. 4 b) each centered on theposition of a hollow.

The position of the markers of the fingertips T1-T5 and of the hollowsbetween each finger C1-C6 is determined by calculating the distancebetween each pixel of the hand contour CT and the center of the palm PC.Thus, FIG. 5 represents a curve 1 of variation of this distance alongthe hand contour CT represented in FIG. 4 a. This curve comprises localminima corresponding to the hollows C1-C6 between each finger, and localmaxima corresponding to the fingertips T1-T5.

The bases of the fingers B1-B5 are then located in each image using theposition of the markers of the hollows C1-C6. For this purpose, the twohollows Cj, Cj+1 of a finger j are identified by searching for the onesclosest to a fingertip Tj considered in the image. The bases of thefingers are then positioned in the middle of the segments [Cj, Cj+1].However, this mode of calculation is not very accurate for certainfingers, and in particular the thumb one of the hollows of which ispoorly located.

Another embodiment of method for locating the base of a finger is shownby FIG. 6. This method comprises selecting out of the two hollows Cj,Cj+1 closest to a fingertip Tj the one closest to the fingertip, i.e.,Cj+1 in the example in FIG. 6. This method then comprises searching forthe point Pj of the hand contour CT located the closest to the selectedhollow Cj+1, and at the same distance from the fingertip Tj as theselected hollow. The base Bj of the finger is then positioned in themiddle of the segment linking the selected hollow Cj+1 to the pointfound Pj.

The module FEXT then determines which finger corresponds to the thumb.For this purpose, the module FEXT applies a test based on thecalculation of the angle between the vector of the forearm PC-AB and thevector of each finger Bj-Tj. The thumb is the finger which has thewidest angle between these two vectors. Other criteria for determiningthe thumb can also be applied alternatively or in combination, like thecomparison of the lengths of the fingers, the thumb being the shortestfinger, and/or of the thicknesses (or widths) of the fingers, the thumbbeing the thickest (or the widest) finger. The length of each finger jcan be determined by calculating the distance Bj-Tj between the markersof the base and of the fingertip. The width of each finger can forexample be obtained by calculating the distance between the points ofthe hand contour CT the closest to a midpoint of the segment Bj-Tj, oran average value of this distance along the segment Bj-Tj.

It shall be noted that if the fingers are squeezed up against oneanother, the hollows Cj between the fingers will be very close to thefingertips Tj. Therefore, the bases of the fingers Bj will also be veryclose to the fingertips. A single finger vector will be positioned inthe image if the hollows of the fingers are not located.

The position of the marker of the beginning of the forearm AB isdetermined by searching for the midpoint of contiguous pixels thefurthest from the point PC and the fingertips Tj using, as applicable,the position of the marker AB in the previous images in the event thatthe fist is clenched. In the event that the contours of the hand extendto the edge of the image, the marker AB is located in the middle of thecontour pixels of the image belonging to the hand contour (case in FIGS.2 c, 4 a, 4 b).

The hand movement following module FOLM uses the skeletal pattern of thehand described previously, positioned in each image by the module FEXT.The global movement of the hand between two successive images isdetermined using the displacement of the markers PC and AB from oneimage to the next. This global movement also supplies an estimation ofthe position of the fingers. The displacement of the fingers is thenestimated by matching each finger marker Bj, Tj from one image to thenext. This matching is performed by minimizing the euclidean distancebetween each finger marker in the successive images, it only beingpossible to match each marker once between two successive images. Whenthe markers of a finger in an image do not correspond to any fingermarker in the previous image, a new finger is created. When the markersof a finger in an image do not correspond to any finger marker in thenext image, the finger is removed. The position of the finger removed issaved for a few images in the image sequence SV in the event that thedisappearance of the finger is temporary (obstruction, or detectionerror).

The module FOLM supplies movement information MI comprising a vector ofdisplacement between two successive images for each marker PC, AB, B1-B5and T1-T5 of the skeletal pattern.

Thanks to the application of a simplified skeletal pattern (location ofthe markers characteristic of the hand PC, AB, T1-T5, B1-B5), the methodaccording to one embodiment enables the movements of a hand observed bya video camera to be followed simply and efficiently. Thus, thenecessary calculation time is very short. The hand can therefore befollowed according to one embodiment in real time by a computer ofstandard PC type connected to a cheap video camera such as a webcam.

The center of the hand PC is located in accordance with the methodaccording to one embodiment whether or not the user's arm is bare. Thepoints characteristic of the hand are located without the need to makean assumption about the entry zone of the hand and irrespective of theposition and orientation of the hand in the image.

One or more embodiments can be applied to the dynamic recognition ofgestures, and in particular but not exclusively to the precise locationof the fingertip and of the direction of the index finger to produce adevice control interface.

Various alternative embodiments and applications of the embodiments maybe made. In particular but not exclusively, the embodiment(s) is notlimited to a location of the contours of the hand based on the color.This location can also be performed by a difference with the backgroundof the image, even if this method is less efficient.

The postural characteristics of the hand can also be determined in otherways than by locating points characteristic of the hand enabling askeletal pattern to be reconstituted. Therefore, the posturalcharacteristics of the hand can comprise searching for a hand contourclose to the one extracted in a library of contours. The posturalcharacteristics of the hand can also be limited to the position of thecenter of the hand, and possibly of the marker of the forearm AB. Theembodiment(s) of the method for determining hand movements to be applieddepends on the manner in which the postural characteristics of the handare defined.

The various embodiments described above can be combined to providefurther embodiments. All of the U.S. patents, U.S. patent applicationpublications, U.S. patent applications, foreign patents, foreign patentapplications and non-patent publications referred to in thisspecification and/or listed in the Application Data Sheet, areincorporated herein by reference, in their entirety. Aspects of theembodiments can be modified, if necessary to employ concepts of thevarious patents, applications and publications to provide yet furtherembodiments.

These and other changes can be made to the embodiments in light of theabove-detailed description. In general, in the following claims, theterms used should not be construed to limit the claims to the specificembodiments disclosed in the specification and the claims, but should beconstrued to include all possible embodiments along with the full scopeof equivalents to which such claims are entitled. Accordingly, theclaims are not limited by the disclosure.

1. A method for following hand movements in an image flow, the methodcomprising: receiving an image flow in real time; locating in each imagein the received image flow a hand contour delimiting an image zone ofthe hand; extracting postural characteristics from the image zone of thehand located in each image; and determining hand movements in the imageflow from the postural characteristics extracted from each image,wherein the extracting the postural characteristics of the hand in eachimage includes locating in the image zone of the hand a center of a palmof the hand by searching for a pixel of the image zone of the hand thatis furthest from the hand contour.
 2. The method according to claim 1wherein locating the center of the palm of the hand includes applying tothe image zone of the hand a distance transform which associates witheach pixel of the image zone of the hand a distance from the pixel to anearest pixel of the hand contour, the center of the palm of the handbeing located on the pixel associated with a greatest distance.
 3. Themethod according to claim 1 wherein the postural characteristics of thehand include positions in the image of fingertips of the hand and ofhollows between each finger, these positions being determined byassociating with each pixel of the hand contour a distance between acontour pixel and the pixel of the center of the palm of the hand, thefingertips being located on the hand contour pixels associated withlocal maxima of variations in a distance with the center of the palm ofthe hand along the hand contour, and the hollows between the fingersbeing located on the hand contour pixels associated with local minima ofvariations in the distance with the center of the palm of the hand alongthe hand contour.
 4. The method according to claim 3 wherein thepostural characteristics of the hand include positions in the image ofbases of the fingers of the hand, a base of each finger being positionedin a middle of a segment delimited by two hollows of the finger.
 5. Themethod according to claim 3 wherein the postural characteristics of thehand include positions in the image of bases of the fingers of the hand,a base of each finger being positioned by: selecting a hollow closest toa fingertip; searching for a point on the hand contour located closestto the selected hollow, and at a same distance from the fingertip as theselected hollow; and calculating a position of the base of the finger ina middle of a segment delimited by the selected hollow and the pointfound.
 6. The method according to claim 4 wherein extracting posturalcharacteristics includes searching for a thumb among fingers identifiedby a base and a fingertip, by calculating an angle between a forearmvector linking a position of the center of the palm of the hand to aposition of a forearm in the image of the hand, and a vector of eachfinger linking a base to the fingertip, the thumb being a finger forminga widest angle with the forearm vector.
 7. The method according to claim4 wherein extracting postural characteristics includes searching for athumb among fingers identified by a base and a fingertip, by determininga length or a width of the fingers, the thumb being a shortest or awidest finger.
 8. The method according to claim 1 wherein the posturalcharacteristics of the hand include a position of a beginning of aforearm which is determined in each image in the image flow by searchingfor a midpoint of contiguous pixels of the hand contour furthest fromthe center of the palm of the hand and from fingertips.
 9. The methodaccording to claim 1 wherein the image zone of the hand in each image inthe image flow is located based on skin color of the hand.
 10. Themethod according to claim 9 wherein a pixel of an image in the imageflow belongs to the image zone of the hand if its color components Cb,Cr in a color space YCbCr meet relations:77≦Cb≦127 and 133≦Cr≦173.
 11. The method according to claim 9 whereinthe hand contour in each image in the image flow is refined bycalculating a probability that each pixel of the image belongs to thehand or not, and by applying a threshold to the probability to obtain abinary image having pixels belonging to the hand or not.
 12. The methodaccording to claim 11 wherein the probability that a pixel i of an imagein the image flow belongs to the hand is calculated using:${p(i)} = \frac{{hskin}(i)}{{htot}(i)}$ wherein hskin(i) and htot(i)represent values of 2D chrominance histograms for chrominance componentsCb, Cr of the pixel i, the histogram hskin being established on theimage zone of the hand, and htot being established on the image in itsentirety.
 13. The method according to claim 11 wherein median filteringand connected component labeling are successively applied to the binaryimage.
 14. The method according to claim 1 wherein a location in eachimage in the received image flow of the hand contour is limited to areduced search zone in the image, determined according to a position ofthe hand contour in a previous image in the image flow.
 15. The methodaccording to claim 3 wherein hand movements in the image flow include aglobal movement determined using a displacement of a position of thecenter of the palm of the hand, and a displacement of each finger of thehand determined by matching each fingertip position between twosuccessive images by minimizing a distance between each fingertipposition in the successive images, each marker being possible to matchonly once between two successive images.
 16. A device for following handmovements in an image flow, the device comprising: means for acquiringan image flow in real time; a location module to locate in each image inthe image flow a hand contour that delimits an image zone of the hand;an extraction module coupled to said location module to extract posturalcharacteristics from the image zone of the hand located in each image;and a following module coupled to said extraction module to determinehand movements in the image flow from the postural characteristicsextracted from each image by the extraction module, wherein theextraction module is configured to determine a position of a center of apalm of the hand in the image zone of the hand, by a search for a pixelof the image zone of the hand that is furthest from the hand contour.17. The device according to claim 16 wherein the extraction module isconfigured to apply to the image zone of the hand a distance transformwhich associates with each pixel of the image zone of the hand adistance from the pixel to the closest pixel of the hand contour, thecenter of the palm of the hand being located on a pixel associated witha greatest distance.
 18. Device according to claim 16 wherein thepostural characteristics of the hand include positions in the image offingertips of the hand and of hollows between each finger, theextraction module being configured to determine these positions byassociating with each pixel of the hand contour a distance between acontour pixel and the pixel of the center of the palm of the hand, thefingertips being located on the hand contour pixels associated withlocal maxima of variations in a distance with the center of the palm ofthe hand along the hand contour, and the hollows between the fingersbeing located on the hand contour pixels associated with local minima ofvariations in the distance with the center of the palm of the hand alongthe hand contour.
 19. The device according to claim 18 wherein thepostural characteristics of the hand include positions in the image ofbases of the fingers of the hand, the extraction module being configuredto determine a position of the base of each finger in the image zone ofthe hand by a search for a middle of a segment delimited by two hollowsof the finger.
 20. The device according to claim 18 wherein the posturalcharacteristics of the hand include positions in the image of bases ofthe fingers of the hand, the extraction module being configured todetermine the position of a base of each finger: by selection of ahollow closest to a fingertip; by search for a point on the hand contourlocated closest to the selected hollow, and at a same distance from thefingertip as the selected hollow; and by calculation of a position ofthe base of the finger in a middle of a segment delimited by theselected hollow and the point found.
 21. The device according to claim19 wherein the extraction module is configured to search for a thumbamong fingers identified by a base and a fingertip, by calculation of anangle between a forearm vector linking a position of the center of thepalm of the hand to a position of a forearm in the image of the hand,and a vector of each finger linking a base to the fingertip, the thumbbeing a finger forming a widest angle with the forearm vector.
 22. Thedevice according to claim 19 wherein the extraction module is configuredto search for a thumb among fingers identified by a base and afingertip, by determination of a length or a thickness of the fingers,the thumb being a longest or a widest finger.
 23. The device accordingto claim 16 wherein the postural characteristics of the hand include aposition of a beginning of a forearm, the extraction module beingconfigured to determine this position by a search for a midpoint ofcontiguous pixels of the hand contour furthest from the center of thepalm of the hand and from fingertips.
 24. The device according to claim16 wherein the location module is configured to locate the image zone ofthe hand in each image in the image flow based on skin color of thehand.
 25. The device according to claim 24 wherein a pixel of an imagein the image flow belongs to the image zone of the hand if its colorcomponents Cb, Cr in a color space YCbCr meet relations:77≦Cb≦127 and 133≦Cr≦173.
 26. The device according to claim 24 whereinthe location module is configured to refine the hand contour in eachimage in the image flow by calculation of a probability that each pixelof the image belongs to the hand or not, and by application of athreshold to the probability to obtain a binary image having pixelsbelonging to the hand or not.
 27. The device according to claim 26wherein the probability that a pixel i of an image in the image flowbelongs to the hand is calculated using:${p(i)} = \frac{{hskin}(i)}{{htot}(i)}$ wherein hskin(i) and htot(i)represent values of 2D chrominance histograms for chrominance componentsCb, Cr of the pixel i, the histogram hskin being established on theimage zone of the hand, and htot being established on the image in itsentirety.
 28. The device according to claim 26 wherein the locationmodule is configured to successively apply median filtering andconnected component labeling to the binary image.
 29. The deviceaccording to claim 16 wherein the location module is configured tolocate the hand contour in a reduced search zone determined according toa position of the hand contour in a previous image in the image flow.30. The device according to claim 18 wherein the hand movement followingmodule is configured to determine a global movement of the hand using adisplacement of a position of the center of the palm of the hand, and todetermine a displacement of each finger of the hand by matching eachfingertip position between two successive images by minimizing adistance between each fingertip position in the successive images, eachmarker being possible to match only once between two successive images.31. The device of claim 16 wherein said means for acquiring the imageflow includes a video camera.
 32. A system, comprising: a device adaptedto follow hand movements in an image flow received in real time andhaving: means for locating in each image in the received image flow ahand contour delimiting an image zone of a hand; means for extractingpostural characteristics from the image zone of the hand located in eachimage; and means for determining hand movements in the image flow fromthe postural characteristics extracted from each image, wherein saidmeans for extracting the postural characteristics of the hand in eachimage locates in the image zone a center of a palm of the hand bysearching for a pixel of the image zone that is furthest from the handcontour.
 33. The system of claim 32, further comprising imageacquisition means for providing said image flow in real time to saiddevice.
 34. The system of claim 32, further comprising controllabledevice means for receiving movement information provided by said meansfor determining and being controllable by said received movementinformation.
 36. The system of claim 32 wherein said means forextracting postural characteristics determines positions of: fingertipsof the hand, bases of fingers, hollows between the fingers, a forearm,and a thumb.
 37. The system of claim 32 wherein said means for locatinglocates the image zone of the hand in each image in the image flow basedon skin color of the hand.
 38. The system of claim 32 wherein said meansfor locating locates the hand contour in a reduced search zonedetermined according to a position of the hand contour in a previousimage in the image flow.
 39. The system of claim 32 wherein said meansfor determining determines a global movement of the hand using adisplacement of a position of the center of the palm of the hand, anddetermines a displacement of each finger of the hand by matching eachfingertip position between two successive images by minimizing adistance between each fingertip position in the successive images.